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Updated: Sep 10, 2025

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel Segmentation.

Giulia Rotunno¹, Massimo Salvi¹, Julia Deinsberger²

¹PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy.

Scientific Data

|August 23, 2025

Summary

This summary is machine-generated.

We introduce DERMA-OCTA, the first open-access dataset for dermatological optical coherence tomography angiography (OCTA) imaging. This resource aids in developing automated analysis tools for non-invasive skin vascular imaging.

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Area of Science:

Dermatology
Medical Imaging
Computer Vision

Background:

Optical coherence tomography angiography (OCTA) is a key non-invasive technique for skin vascular imaging.
Standardized analysis methods and annotated datasets are lacking for dermatological OCTA.
Automated analysis tools require extensive, high-quality annotated data for development.

Purpose of the Study:

To introduce DERMA-OCTA, the first open-access dataset for dermatological OCTA.
To provide a comprehensive resource for training deep learning models and benchmarking algorithms.
To facilitate research in non-invasive skin vascular imaging.

Main Methods:

Collected 330 volumetric OCTA scans from 74 subjects with diverse skin conditions.
Included original 2D and 3D OCTA data, processed versions (5 methods), and reference segmentations.
Generated 2D and 3D segmentation labels using U-Net architecture.

Main Results:

Established the DERMA-OCTA dataset, featuring high-resolution annotated OCTA data.
Dataset includes raw and processed scans with detailed segmentation labels for various skin pathologies.
The U-Net architecture was employed for generating both 2D and 3D segmentation masks.

Conclusions:

DERMA-OCTA is a valuable, freely downloadable resource for advancing dermatological OCTA research.
The dataset supports the development of deep learning models for automated skin vascular analysis.
Facilitates benchmarking of segmentation algorithms and promotes non-invasive skin imaging research.